from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
%matplotlib notebook
print_time_report()
daal4py_KMeans_short: 0h 0m 1s
daal4py_Ridge: 0h 0m 2s
KMeans_short: 0h 0m 3s
daal4py_LogisticRegression: 0h 0m 4s
daal4py_KMeans_tall: 0h 0m 8s
Ridge: 0h 0m 11s
LogisticRegression: 0h 0m 20s
KMeans_tall: 0h 0m 23s
daal4py_KNeighborsClassifier_kd_tree: 0h 0m 28s
KNeighborsClassifier_kd_tree: 0h 2m 37s
daal4py_KNeighborsClassifier: 0h 2m 47s
lightgbm: 0h 5m 1s
HistGradientBoostingClassifier: 0h 5m 1s
catboost_symmetric: 0h 5m 2s
xgboost: 0h 5m 4s
catboost_lossguide: 0h 5m 7s
KNeighborsClassifier: 0h 33m 38s
total: 1h 6m 4s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.8.0-1033-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.21.0",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.5",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.154 | 0.000 | 5.195 | 0.000 | 1 | 5 | NaN | NaN | 0.517 | 0.000 | 0.298 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.999 | 0.165 | 0.000 | 0.023 | 1 | 5 | 0.788 | 0.939 | 2.103 | 0.013 | 10.935 | 0.102 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.219 | 0.001 | 0.000 | 0.219 | 1 | 5 | 1.000 | 1.000 | 0.086 | 0.002 | 2.532 | 0.075 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.127 | 0.000 | 6.306 | 0.000 | 1 | 1 | NaN | NaN | 0.507 | 0.000 | 0.250 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 12.749 | 0.062 | 0.000 | 0.013 | 1 | 1 | 0.690 | 0.806 | 1.983 | 0.026 | 6.429 | 0.089 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.205 | 0.003 | 0.000 | 0.205 | 1 | 1 | 0.000 | 1.000 | 0.084 | 0.001 | 2.431 | 0.045 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.126 | 0.000 | 6.333 | 0.000 | -1 | 100 | NaN | NaN | 0.478 | 0.000 | 0.264 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 34.480 | 0.000 | 0.000 | 0.034 | -1 | 100 | 0.939 | 0.806 | 1.936 | 0.020 | 17.808 | 0.185 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.205 | 0.020 | 0.000 | 0.205 | -1 | 100 | 0.000 | 1.000 | 0.083 | 0.000 | 2.487 | 0.239 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.129 | 0.000 | 6.220 | 0.000 | -1 | 5 | NaN | NaN | 0.468 | 0.000 | 0.275 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 34.383 | 0.000 | 0.000 | 0.034 | -1 | 5 | 0.788 | 0.699 | 1.926 | 0.029 | 17.852 | 0.269 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.207 | 0.019 | 0.000 | 0.207 | -1 | 5 | 1.000 | 1.000 | 0.084 | 0.001 | 2.450 | 0.224 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.127 | 0.000 | 6.315 | 0.000 | 1 | 100 | NaN | NaN | 0.480 | 0.000 | 0.264 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.033 | 0.143 | 0.000 | 0.023 | 1 | 100 | 0.939 | 0.699 | 1.987 | 0.025 | 11.592 | 0.164 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.219 | 0.002 | 0.000 | 0.219 | 1 | 100 | 0.000 | 1.000 | 0.086 | 0.002 | 2.533 | 0.062 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.124 | 0.000 | 6.429 | 0.000 | -1 | 1 | NaN | NaN | 0.481 | 0.000 | 0.259 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.802 | 0.241 | 0.000 | 0.024 | -1 | 1 | 0.690 | 0.939 | 2.070 | 0.051 | 11.499 | 0.305 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.183 | 0.018 | 0.000 | 0.183 | -1 | 1 | 0.000 | 1.000 | 0.087 | 0.003 | 2.110 | 0.228 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.264 | 0.000 | 1 | 5 | NaN | NaN | 0.110 | 0.000 | 0.551 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.029 | 0.181 | 0.000 | 0.021 | 1 | 5 | 0.987 | 0.981 | 0.364 | 0.005 | 57.846 | 0.943 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.026 | 0.001 | 0.000 | 0.026 | 1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 4.120 | 0.317 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.281 | 0.000 | 1 | 1 | NaN | NaN | 0.111 | 0.000 | 0.513 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 9.870 | 0.045 | 0.000 | 0.010 | 1 | 1 | 0.969 | 0.979 | 0.308 | 0.006 | 32.076 | 0.610 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.000 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.006 | 0.001 | 2.416 | 0.358 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.261 | 0.000 | -1 | 100 | NaN | NaN | 0.108 | 0.000 | 0.566 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 31.978 | 0.000 | 0.000 | 0.032 | -1 | 100 | 0.986 | 0.979 | 0.300 | 0.005 | 106.599 | 1.765 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.034 | 0.002 | 0.000 | 0.034 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.001 | 5.759 | 0.680 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.066 | 0.000 | 0.243 | 0.000 | -1 | 5 | NaN | NaN | 0.107 | 0.000 | 0.613 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 32.127 | 0.000 | 0.000 | 0.032 | -1 | 5 | 0.987 | 0.971 | 0.307 | 0.009 | 104.684 | 3.080 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.034 | 0.002 | 0.000 | 0.034 | -1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 5.942 | 0.475 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.059 | 0.000 | 0.273 | 0.000 | 1 | 100 | NaN | NaN | 0.113 | 0.000 | 0.517 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.824 | 0.076 | 0.000 | 0.021 | 1 | 100 | 0.986 | 0.971 | 0.306 | 0.006 | 68.044 | 1.402 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.027 | 0.001 | 0.000 | 0.027 | 1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 4.629 | 0.414 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.269 | 0.000 | -1 | 1 | NaN | NaN | 0.107 | 0.000 | 0.556 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.203 | 0.169 | 0.000 | 0.021 | -1 | 1 | 0.969 | 0.981 | 0.364 | 0.020 | 58.288 | 3.302 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.023 | 0.003 | 0.000 | 0.023 | -1 | 1 | 1.000 | 1.000 | 0.006 | 0.001 | 3.798 | 0.936 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.906 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.749 | 0.000 | 3.877 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.448 | 0.008 | 0.000 | 0.000 | -1 | 1 | 0.961 | 0.956 | 0.101 | 0.001 | 4.432 | 0.103 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 14.766 | 7.398 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.977 | 0.000 | 0.027 | 0.000 | 1 | 5 | NaN | NaN | 0.736 | 0.000 | 4.044 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.369 | 0.013 | 0.000 | 0.001 | 1 | 5 | 0.971 | 0.971 | 0.550 | 0.009 | 2.491 | 0.047 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 2.056 | 0.932 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.916 | 0.000 | 0.027 | 0.000 | 1 | 100 | NaN | NaN | 0.743 | 0.000 | 3.924 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.591 | 0.061 | 0.000 | 0.005 | 1 | 100 | 0.968 | 0.974 | 0.183 | 0.005 | 25.119 | 0.804 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 16.013 | 7.965 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.988 | 0.000 | 0.027 | 0.000 | 1 | 1 | NaN | NaN | 0.712 | 0.000 | 4.194 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.717 | 0.012 | 0.000 | 0.001 | 1 | 1 | 0.961 | 0.971 | 0.544 | 0.014 | 1.319 | 0.041 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 1.834 | 0.705 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.949 | 0.000 | 0.027 | 0.000 | -1 | 5 | NaN | NaN | 0.710 | 0.000 | 4.156 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.837 | 0.011 | 0.000 | 0.001 | -1 | 5 | 0.971 | 0.974 | 0.187 | 0.011 | 4.470 | 0.259 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.000 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 11.837 | 5.315 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.089 | 0.000 | 0.026 | 0.000 | -1 | 100 | NaN | NaN | 0.767 | 0.000 | 4.025 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.803 | 0.039 | 0.000 | 0.003 | -1 | 100 | 0.968 | 0.956 | 0.100 | 0.002 | 28.009 | 0.775 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.008 | 0.002 | 0.000 | 0.008 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 34.520 | 18.065 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.856 | 0.000 | 0.019 | 0.000 | -1 | 1 | NaN | NaN | 0.527 | 0.000 | 1.625 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.982 | 0.973 | 0.001 | 0.000 | 27.552 | 12.738 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 19.757 | 13.422 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.859 | 0.000 | 0.019 | 0.000 | 1 | 5 | NaN | NaN | 0.494 | 0.000 | 1.739 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.985 | 0.981 | 0.007 | 0.002 | 3.234 | 0.776 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.926 | 2.954 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.809 | 0.000 | 0.020 | 0.000 | 1 | 100 | NaN | NaN | 0.482 | 0.000 | 1.679 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.050 | 0.002 | 0.000 | 0.000 | 1 | 100 | 0.985 | 0.980 | 0.002 | 0.001 | 32.420 | 10.635 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.384 | 3.823 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.810 | 0.000 | 0.020 | 0.000 | 1 | 1 | NaN | NaN | 0.473 | 0.000 | 1.712 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.022 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.982 | 0.981 | 0.008 | 0.001 | 2.891 | 0.345 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.939 | 3.204 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.812 | 0.000 | 0.020 | 0.000 | -1 | 5 | NaN | NaN | 0.504 | 0.000 | 1.610 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.028 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.985 | 0.980 | 0.002 | 0.002 | 13.240 | 11.240 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.001 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 20.834 | 14.041 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.864 | 0.000 | 0.019 | 0.000 | -1 | 100 | NaN | NaN | 0.470 | 0.000 | 1.836 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.047 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.985 | 0.973 | 0.001 | 0.000 | 48.940 | 17.801 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 20.884 | 15.259 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.663 | 0.0 | 0.724 | 0.000 | random | NaN | 30 | NaN | 0.480 | 0.0 | 1.382 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.338 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 9.288 | 5.845 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.920 | 7.182 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.616 | 0.0 | 0.780 | 0.000 | k-means++ | NaN | 30 | NaN | 0.411 | 0.0 | 1.497 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.339 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 9.277 | 5.528 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.538 | 6.769 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.705 | 0.0 | 3.580 | 0.000 | random | NaN | 30 | NaN | 2.937 | 0.0 | 2.283 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 12.398 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 6.555 | 3.036 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.017 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.911 | 6.260 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.439 | 0.0 | 3.727 | 0.000 | k-means++ | NaN | 30 | NaN | 2.803 | 0.0 | 2.297 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 13.823 | 0.000 | k-means++ | 0.001 | 30 | 0.002 | 0.000 | 0.0 | 5.976 | 2.635 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.017 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.158 | 5.086 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.087 | 0.000 | 0.037 | 0.000 | random | NaN | 20 | NaN | 0.103 | 0.0 | 0.843 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.163 | 0.000 | random | 0.000 | 20 | 0.002 | 0.001 | 0.0 | 2.782 | 0.579 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.958 | 5.268 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.238 | 0.000 | 0.013 | 0.000 | k-means++ | NaN | 20 | NaN | 0.039 | 0.0 | 6.110 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.167 | 0.000 | k-means++ | -0.000 | 20 | 0.004 | 0.001 | 0.0 | 2.461 | 0.438 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.180 | 5.101 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.217 | 0.000 | 0.737 | 0.000 | random | NaN | 20 | NaN | 0.377 | 0.0 | 0.577 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 6.005 | 0.000 | random | 0.307 | 20 | 0.311 | 0.001 | 0.0 | 1.781 | 0.444 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.011 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.596 | 4.143 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.660 | 0.000 | 0.242 | 0.000 | k-means++ | NaN | 20 | NaN | 0.148 | 0.0 | 4.450 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.001 | 4.923 | 0.000 | k-means++ | 0.350 | 20 | 0.323 | 0.001 | 0.0 | 2.338 | 0.654 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.010 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.933 | 5.711 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.902 | 0.0 | [-0.09913314] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.931 | 0.0 | 6.163 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [52.15511454] | 0.000 | NaN | NaN | NaN | NaN | 0.549 | 0.000 | 0.0 | 0.853 | 0.422 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.20150651] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.426 | 0.350 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [27] | 0.778 | 0.0 | [-2.74375519] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.711 | 0.0 | 1.095 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [27] | 0.002 | 0.0 | [118.75225032] | 0.000 | NaN | NaN | NaN | NaN | 0.160 | 0.003 | 0.0 | 0.583 | 0.125 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [27] | 0.000 | 0.0 | [20.4715081] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.108 | 0.078 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.187 | 0.000 | 0.428 | 0.0 | NaN | NaN | NaN | 0.191 | 0.000 | 0.976 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.001 | 8.015 | 0.0 | NaN | NaN | 0.105 | 0.017 | 0.001 | 0.600 | 0.043 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 0.871 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.741 | 0.700 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.546 | 0.000 | 0.518 | 0.0 | NaN | NaN | NaN | 0.260 | 0.000 | 5.939 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 5.185 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.685 | 0.451 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.012 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.660 | 0.610 | See | See |